Activity Detection in Videos. Riu Baring CIS 8590 Perception of Intelligent System Temple University Fall 2007. Outline. Background Related Work The Model Normal Count Event Count. Activity Detection Problems.
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CIS 8590 Perception of Intelligent System
a) changes due to noise; i.e., those best modeled by e.g., a Gaussian error distribution.
b) periodic changes; i.e., those expected to happen over periodic intervals.
c) changes not due to either of the above: these are usually the changes we would like to detect.
To automatically detect
the presence of unusual events
in the observation sequence.
Event Count (Unobserved )
Normal Count (Unobserved)
Poisson Process Rate
Time of Day Effect
i.e., if there is no event at time t-1, the chance of an event at time t is 1-z00
Modeling Rare Persistent Events
and z11 analogously.
This characterizes the behavior of the underlying latent process. The hyperparameters a,b are designed to model that behavior.
Recall that N0(t) (the non-event process) characterizes periodic and noise changes. The event process NE(t) characterizes other changes.
NE(t) is 0 if z(t)=0 and Poisson with rate γ(t) if z(t)=1.
So, if there is no event, N(t)=N0(t). If there is an event, the frequency due to periodic or noise changes is N(t)=N0(t)+NE(t)
The rate γ(t) is itself gamma with parameters aE and bE. Hence (by conjugacy) it is marginally negative binomial (NB) with p=(bE/(1+bE) and n=N(t).